
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import itertools
import collections
import re
import statistics


source_file = open("Group_2.csv", "r") #original file
df_to_append = []
remove_from_lines = ['"','+','0000']
count = 0
for line in source_file:
    line = line.replace(",", " ")

    for word in remove_from_lines:
        line = line.replace(word,'')

    tweet_date = line[line.find("{created_at") + 12 : line.find("id:")] 
    index = line.index(tweet_date)
    line = line [index : :]

    tweet_id_no = line[line.find("id:") + 3 : line.find("id_str:")]
    index = line.index(tweet_id_no)
    line = line [index : :]

    tweet_id = line[line.find("id_str:")+7:line.find("text:")]
    index = line.index(tweet_id)
    line = line [index : :]
    
    tweet_text = line[line.find("text:")+5:line.find("source:")]
    index = line.index(tweet_text)
    line = line[index : :]

    tweet_source = line[line.find("source:") + 7 : line.find("truncated:")]
    index = line.index(tweet_source)
    line = line[index : :]

    truncated = line[line.find("truncated:") + 10 : line.find("in_reply_to_status_id:")]
    index = line.index(truncated)
    line = line[index : :]

    in_reply_to_status_id = line[line.find("in_reply_to_status_id:") + 22 : line.find("in_reply_to_status_id_str:")]
    index = line.index(in_reply_to_status_id)
    line = line[index : :]
    
    in_reply_to_status_id_str = line[line.find("in_reply_to_status_id_str:") + 26 : line.find("in_reply_to_user_id:")]
    index = line.index(in_reply_to_status_id_str)
    line = line[index : :]

    in_reply_to_user_id = line[line.find("in_reply_to_user_id:") + 20 : line.find("in_reply_to_user_id_str:")]
    index = line.index(in_reply_to_user_id)
    line = line[index : :]

    in_reply_to_screen_name = line[line.find("in_reply_to_screen_name:") + 24 : line.find("user:")]
    index = line.index(in_reply_to_screen_name)
    line = line[index : :]

    useridIndex = line.index(line[line.find("user:{id:")::])
    additional = useridIndex-index
    line = line[index+additional : :]
    
    user_id_str = line[line.find("id_str") + 7 : line.find("name:")]
    index = line.index(user_id_str)
    line = line [index : :]

    user_name = line[line.find("name:") + 5 : line.find("screen_name:")]
    index = line.index(user_name)
    line = line [index : :]

    user_screen_name = line[line.find("screen_name:") + 12 : line.find("location:")]
    index = line.index(user_screen_name)
    line = line [index : :]

    location = line[line.find("location:")+9:line.find("url:")]
    index = line.index(location)
    line = line[index : :]

    url = line[line.find("url:") + 4 : line.find("description:")]
    index = line.index(url)
    line = line[index : :]

    description = line[line.find("description:")+12:line.find("translator_type:")]
    index = line.index(description)
    line = line[index : :]

    translator_type = line[line.find("translator_type:") +16 : line.find("protected:")]
    index = line.index(translator_type)
    line = line[index : :]

    protected_status = line[line.find("protected:") +16 : line.find("verified:")]
    index = line.index(protected_status)
    line = line[index : :]

    verified = line[line.find("verified:")+9:line.find("followers_count:")]
    index = line.index(verified)
    line = line[index : :]

    followers_count = line[line.find("followers_count:")+16:line.find("friends_count:")]
    index = line.index(followers_count)
    line = line[index : :]

    friends_count = line[line.find("friends_count:")+14:line.find("listed_count:")]
    index = line.index(friends_count)
    line = line[index : :]

    listed_count = line[line.find("listed_count:")+13:line.find("favourites_count:")]
    index = line.index(listed_count)
    line = line[index : :]

    cumulative_favorites = line[line.find("favourites_count:")+17:line.find("statuses_count:")]
    index = line.index(cumulative_favorites)
    line = line[index : :]
    
    statuses_count = line[line.find("statuses_count:")+15:line.find("created_at:")]
    index = line.index(statuses_count)
    line = line[index : :]

    account_creation_date = line[line.find("created_at:")+11:line.find("utc_offset:")]
    index = line.index(account_creation_date)
    line = line[index : :]

    utc_offset = line[line.find("utc_offset:") + 11 : line.find("time_zone:")]
    index = line.index(account_creation_date)
    line = line[index : :]

    time_zone = line[line.find("time_zone:") + 10 : line.find("geo_enabled:")]
    index = line.index(time_zone)
    line = line[index : :]

    geo_enabled = line[line.find("geo_enabled:") + 12 :line.find("lang:")]
    index = line.index(geo_enabled)
    line = line[index : :]

    language = line[line.find("lang:") + 5 : line.find("contributors_enabled")]
    index = line.index(language)
    line = line[index : :]

    contributors_enabled = line[line.find("contributors_enabled:") + 21 : line.find("is_translator:")]
    index = line.index(contributors_enabled)
    line = line[index : :]

    is_translator = line[line.find("is_translator:") + 14 : line.find("profile_background_color:")]
    index = line.index(is_translator)
    line = line[index : :]

    following = line[line.find("following:") + 10 : line.find("follow_request_sent:")]
    index = line.index(following)
    line = line [index : :]

    follow_request_sent = line[line.find("follow_request_sent:") + 20 : line.find("notifications")]
    index = line.index(follow_request_sent)
    line = line [index : :]

    notifications = line[line.find("notifications:") + 14 : line.find("}")]
    index = line.index(notifications)
    line = line [index : :]

    coordinates = line[line.find("coordinates:") + 12 : line.find("place")]
    index = line.index(coordinates)
    line = line [index : :]

    place = line[line.find("place:") + 6 : line.find("contributors")]
    index = line.index(place)
    line = line [index : :]

    
    extended_tweet = line[line.find("text:") + 5 : line.find("display_text_range:")]
    index = line.index(extended_tweet)
    line = line [index : :]

    entities = line[line.find("text:")+5:line.find("indices:")]
    index = line.index(entities)
    line = line [index : :]

    reply_count = line[line.find("reply_count:") + 12 : line.find("retweet_count:")]
    index = line.index(reply_count)
    line = line [index : :]

    if len(reply_count) > 6:
        reply_count = np.NAN

    retweet_count = line[line.find("retweet_count:")+14:line.find("favorite_count")]
    index = line.index(retweet_count)
    line = line[index : :]

    favorite_count= line[line.find("favorite_count:")+15:line.find("entities:")]
    index = line.index(favorite_count)
    line = line[index : :]

    df_to_append.append([tweet_date,tweet_id_no, tweet_id,tweet_text,tweet_source,truncated,in_reply_to_status_id,
                in_reply_to_status_id_str,in_reply_to_user_id,in_reply_to_screen_name,user_id_str,user_name,user_screen_name,location,url,description,
                translator_type, protected_status,verified,followers_count,friends_count,listed_count,cumulative_favorites,
                account_creation_date,statuses_count,utc_offset, time_zone, geo_enabled,language, contributors_enabled, is_translator,
                following, follow_request_sent, notifications, coordinates, place,extended_tweet, entities, reply_count,
                retweet_count,favorite_count])
source_file.close()

df = pd.DataFrame(df_to_append, columns= ['Tweet_date','Tweet_id_No', 'Tweet_id_str','Tweet_text','Tweet_Source',
    'Truncated_Status','Reply_to_Status_ID','Reply_to_status_ID_Str','Reply_to_USER_id','Reply_to_screen_name','User_id',
    'Name of User', 'Screen Name of user','Location','URL','Description','translator_type', 'protected_status','Verified',
    'Followers_count','Friends_count','Listed_count','Cumulative_favorites', 'Account_creation_date','Statuses_count',
    'utc_offset','time_zone','geo_enabled_status','Language','contributors_enabled', 'is_translator',
    'following', 'follow_request_sent','notifications','coordinates','place', 'Extended_tweet', 'Entities', 'Reply_count', 'Retweet_count','Favorites_count'])

# print(df.iloc[1:2000 , 38:39 ])

df.to_csv('Cleaned_Data.csv', index = False, sep =',')

#Formatting the Output

cleaned_data = open("Cleaned_Data.csv","r")

cleaned_formatted=open("Final Formatted File.csv","w")

for line in cleaned_data:
    if line[0] == ",":
        continue
    cleaned_formatted.write(line)

cleaned_data.close()
cleaned_formatted.close()

os.remove("Cleaned_Data.csv")


df = pd.read_csv('Final Formatted File.csv')

df


#pip install missingno


import missingno as msno
  
# Visualize missing values as a matrix
msno.matrix(df)
print('This bar chart attached below tells how many missing values are there in each column of our dataset. More white space represetns more nan values. The given results helped us dropping columns with high significantly high number of null values at a later stage of the analysis.')

This bar chart attached below tells how many missing values are there in each column of our dataset. More white space represetns more nan values. The given results helped us dropping columns with high significantly high number of null values at a later stage of the analysis.


def remove_usernames(text):
    return re.sub('(?<=^|(?<=[^a-zA-Z0-9-_\.]))@([A-Za-z]+[A-Za-z0-9-_]+)', '', text)


def remove_hyperlinks(text):
    return re.sub("((http|https)\:\/\/)?[a-zA-Z0-9\.\/\?\:@\-_=#]+\.([a-zA-Z]){2,6}([a-zA-Z0-9\.\&\/\?\:@\-_=#])*", '', text)


def remove_punctuation(text):
    return re.sub("[^a-zA-Z]+", " ", str(text))


def to_lower_case(text):
    return text.lower()


from nltk.stem import PorterStemmer
porter_stemmer=PorterStemmer()
def stemming(text):
    words=re.split("\\s+",text)
    stemmed_words=[porter_stemmer.stem(word=word) for word in words]
    return ' '.join(stemmed_words)


#some null values were stored as a string. Converted them back to NaN using np.nan
count=0
columns = df.columns.to_list()
for x in range(len(columns)):
    lista = df[columns[x]].to_list()
    size = len(lista)
    listb=[]
    for i in range(size):
        if (lista[i]=="null ") or (lista[i]=="null") or (lista[i]==" null") or lista[i]=="nan" or lista[i]==" nan" or lista[i]=="nan ":
            lista[i]=np.nan
    df[columns[x]]=lista
df.isnull().sum()

Tweet_date                     2
Tweet_id_No                    0
Tweet_id_str                   0
Tweet_text                     0
Tweet_Source                   0
Truncated_Status               0
Reply_to_Status_ID         57208
Reply_to_status_ID_Str     57208
Reply_to_USER_id           56749
Reply_to_screen_name       56750
User_id                   123963
Name of User              123963
Screen Name of user       123963
Location                  143194
URL                       172365
Description               135703
translator_type           123963
protected_status          185945
Verified                  123963
Followers_count           123963
Friends_count             123963
Listed_count              123963
Cumulative_favorites      123963
Account_creation_date     123963
Statuses_count            123963
utc_offset                185945
time_zone                 185945
geo_enabled_status        123963
Language                  185945
contributors_enabled      123963
is_translator             123963
following                 185945
follow_request_sent       185945
notifications             185945
coordinates               185923
place                     185415
Extended_tweet            123963
Entities                  124803
Reply_count               124853
Retweet_count             123963
Favorites_count           129073
dtype: int64


#Exploration of columns in the dataset 
df.columns

Index(['Tweet_date', 'Tweet_id_No', 'Tweet_id_str', 'Tweet_text',
       'Tweet_Source', 'Truncated_Status', 'Reply_to_Status_ID',
       'Reply_to_status_ID_Str', 'Reply_to_USER_id', 'Reply_to_screen_name',
       'User_id', 'Name of User', 'Screen Name of user', 'Location', 'URL',
       'Description', 'translator_type', 'protected_status', 'Verified',
       'Followers_count', 'Friends_count', 'Listed_count',
       'Cumulative_favorites', 'Account_creation_date', 'Statuses_count',
       'utc_offset', 'time_zone', 'geo_enabled_status', 'Language',
       'contributors_enabled', 'is_translator', 'following',
       'follow_request_sent', 'notifications', 'coordinates', 'place',
       'Extended_tweet', 'Entities', 'Reply_count', 'Retweet_count',
       'Favorites_count'],
      dtype='object')


#Dropping columns due to high presence of null_values
#removing Tweet_id_str column due to repitition of same data / reducing data redundancy
df = df.drop(columns=['Reply_to_Status_ID', 'Reply_to_status_ID_Str','Reply_to_USER_id','Reply_to_screen_name','protected_status','utc_offset','time_zone','Language','following','follow_request_sent','notifications','coordinates','place','Tweet_id_str'])
df


df.drop_duplicates(subset ="Tweet_id_No",keep = False, inplace = True)
df.shape

(61934, 27)


msno.matrix(df)

<AxesSubplot:>


total_tweets = df['Tweet_id_No'].unique()
total_tweets1=len(total_tweets)
print("Total Number of tweets = ",total_tweets1)
urltweets = df['URL']
print("Tweets with URL = ", total_tweets1-urltweets.isnull().sum())
ver_tweets = df[df["Verified"]=='true '].shape[0]
nonver_tweets = total_tweets1-ver_tweets
print("Verified Tweets: ",ver_tweets, " Non-Verified Tweets: ",nonver_tweets)
tweets_greater_500_followers = df[df['Followers_count']>=500].shape[0]
tweets_less_500_followers = total_tweets1-tweets_greater_500_followers
print("Tweets made by accounts who had greater than or equal to 500 followers: ",tweets_greater_500_followers)
print("Tweets made by accounts who had less than 500 followers: ", tweets_less_500_followers)

bcount=0
subject_list = df["Entities"].to_list()
for j in range(len(subject_list)):
    res1 = str(subject_list[j]).find('Brexit')
    res2 =  str(subject_list[j]).find('brexit')
    if (res1 != -1) or (res2 !=-1):
        bcount=bcount+1
#print("Number of tweets which contain brexit in their text: ",bcount)
#Brexit data proven

#cleaning reply count column
#replies = df['Reply_count'].to_list()
#replies1=[]
#for l in range(len(replies)):
#    res3=str(replies[l])
#    if len(res3)<10:
#        replies1.append(int(res3))
#    else:
#        replies1.append(np.nan)
#df['Reply_count']=replies1
number_of_replies_greater_1000 = df[df['Reply_count']>1000].shape[0]
print("Tweets with more than 1000 replies: ",number_of_replies_greater_1000)

Total Number of tweets =  61934
Tweets with URL =  13576
Verified Tweets:  1142  Non-Verified Tweets:  60792
Tweets made by accounts who had greater than or equal to 500 followers:  33500
Tweets made by accounts who had less than 500 followers:  28434
Tweets with more than 1000 replies:  3332


#cleaning
relist = df["Retweet_count"].to_list()
relist1 = []
for x in range(len(relist)):
    if len(relist[x])<10:
       relist1.append(int(relist[x]))
    else:
        relist1.append(np.nan)
df["Retweet_count"]=relist1


print('Total Tweets: ', total_tweets1)
unique_accounts = len(df.User_id.unique())
retweets_sum = df["Retweet_count"].sum()
print("Total number of Retweets: ",int(retweets_sum))
print('Total Unique Users: ', unique_accounts)
unique_tweet_timings = df.Tweet_date.unique()
print("Number of tweets containing a URL: ",total_tweets1-urltweets.isnull().sum()) # calculate above
#Number of tweets that are replies
t_count=0
t_c=0
tstring = df['Tweet_text'].to_list()
for j in range(len(tstring)):
    stringg=str(tstring[j])
    if (stringg[0:2].find("RT"))!=-1:
        t_count=t_count+1

for p in range(len(tstring)):
    string1=str(tstring[p])
    if (string1[0:2].find("RT"))==-1:
        if (string1[0].find("@"))!=-1:
            t_c=t_c+1
print("Number of Tweets that are replies: ",t_count+t_c)

print('The extracted dataset from twitter comprises of tweets from', unique_tweet_timings[0], 'till', unique_tweet_timings.item(-1))

Total Tweets:  61934
Total number of Retweets:  30996674
Total Unique Users:  30537
Number of tweets containing a URL:  13576
Number of Tweets that are replies:  52288
The extracted dataset from twitter comprises of tweets from Fri Oct 11 07:03:12  2019  till Fri Oct 11 09:26:50  2019


subset1 = df[['Name of User','Followers_count','Verified']]
ver = subset1[subset1['Verified']=='true '].sort_values(by='Followers_count', ascending=False)
#ver.drop_duplicates(subset ="Name of User",keep = False, inplace = True)
abc = ver.groupby('Name of User')['Followers_count'].mean()
abc = pd.DataFrame(abc)
abc = abc.sort_values(by='Followers_count', ascending=False)
abc.head(10)


temp = df.copy()
indexx = [i for i in range (0,len(temp))]
temp.index = indexx


#Analyzing at which hour tweets were at peak
tweets_7_730 = 0
tweets_730_8 = 0
tweets_8_830 = 0
tweets_830_9 = 0
tweets_9_930 = 0

for i in range (0, len(temp['Tweet_date'])-4):
    hour = int((temp['Tweet_date'][i])[11:13])
    minutes = int((temp['Tweet_date'][i])[14:16])
    if hour < 8:
        if minutes < 30:
            tweets_7_730 = tweets_7_730 +1
        else:
            tweets_730_8 = tweets_730_8 +1
    elif hour < 9:
        if minutes < 30:
            tweets_8_830 = tweets_8_830 +1
        else:
            tweets_830_9 = tweets_830_9 +1
    elif hour < 10:
        tweets_9_930 = tweets_9_930 +1 
print('Total tweets between 7 and 7:30:    ', tweets_7_730)
print('Total tweets between 7:30 and 8:    ', tweets_730_8)
print('Total tweets between 8 and 8:30:    ', tweets_8_830)
print('Total tweets between 8:30 and 9:    ', tweets_830_9)
print('Total tweets between 9 and 9:30:    ', tweets_9_930)

Total tweets between 7 and 7:30:     11139
Total tweets between 7:30 and 8:     13557
Total tweets between 8 and 8:30:     12911
Total tweets between 8:30 and 9:     12550
Total tweets between 9 and 9:30:     11773


# Creating dataset
time_frame = ["7 to 7:30", "7:30 to 8", "8 to 8:30", "8:30 to 9", "9 to 9:30"]
  
no_tweets = [tweets_7_730, tweets_730_8, tweets_8_830, tweets_830_9, tweets_9_930]
  
explode = (0.1, 0.0, 0.2, 0.3, 0.0)
  
colors = ( "orange", "cyan", "brown",
          "blue", "indigo", "beige")
  
wp = { 'linewidth' : 1, 'edgecolor' : "green" }
  
def func(pct, allvalues):
    absolute = int(pct / 100.*np.sum(allvalues))
    return "{:.1f}%\n({:d} g)".format(pct, absolute)
  
# Creating plot
fig, ax = plt.subplots(figsize =(10, 7))
wedges, texts, autotexts = ax.pie(no_tweets, 
                                  autopct = lambda pct: func(pct, no_tweets),
                                  explode = explode, 
                                  labels = time_frame,
                                  shadow = True,
                                  colors = colors,
                                  startangle = 90,
                                  wedgeprops = wp,
                                  textprops = dict(color ="magenta"))
  
# Adding legend
ax.legend(wedges, time_frame,
          title ="Time Frame",
          loc ="center left",
          bbox_to_anchor =(1, 0, 0.5, 1))
  
plt.setp(autotexts, size = 8, weight ="bold")
ax.set_title("Number of Tweets in Given Time")
  
# show plot
plt.show()


# Replacing nans with Not Disclosed
values = {'Location': 'Not Disclosed'}
temp = temp.fillna(value=values)
temp['Location']
temp.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 61934 entries, 0 to 61933
Data columns (total 27 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   Tweet_date             61934 non-null  object 
 1   Tweet_id_No            61934 non-null  object 
 2   Tweet_text             61934 non-null  object 
 3   Tweet_Source           61934 non-null  object 
 4   Truncated_Status       61934 non-null  object 
 5   User_id                61934 non-null  float64
 6   Name of User           61934 non-null  object 
 7   Screen Name of user    61934 non-null  object 
 8   Location               61934 non-null  object 
 9   URL                    13576 non-null  object 
 10  Description            50212 non-null  object 
 11  translator_type        61934 non-null  object 
 12  Verified               61934 non-null  object 
 13  Followers_count        61934 non-null  float64
 14  Friends_count          61934 non-null  float64
 15  Listed_count           61934 non-null  float64
 16  Cumulative_favorites   61934 non-null  float64
 17  Account_creation_date  61934 non-null  object 
 18  Statuses_count         61934 non-null  float64
 19  geo_enabled_status     61934 non-null  object 
 20  contributors_enabled   61934 non-null  object 
 21  is_translator          61934 non-null  object 
 22  Extended_tweet         61934 non-null  object 
 23  Entities               61096 non-null  object 
 24  Reply_count            61046 non-null  float64
 25  Retweet_count          61046 non-null  float64
 26  Favorites_count        56826 non-null  object 
dtypes: float64(8), object(19)
memory usage: 15.7+ MB


#Cleaning and Standardizing Location of the people with Tweets
temp['Location'] = temp['Location'].apply(remove_punctuation)


for i in range(0, len(temp.Location)):
    if(temp.iloc[i][8] == 'London England '):
        temp.at[i,'Location'] = 'London '
    elif(temp.iloc[i][8] == 'England United Kingdom '):
        temp.at[i,'Location'] = 'England '
    elif(temp.iloc[i][8] == 'UK '):
        temp.at[i,'Location'] = 'United Kingdom '
    elif(temp.iloc[i][8] == 'Scotland United Kingdom '):
        temp.at[i,'Location'] = 'Scotland '
    elif(temp.iloc[i][8] == 'Manchester England '):
        temp.at[i,'Location'] = 'Manchester '
    elif(temp.iloc[i][8] == 'London UK '):
        temp.at[i,'Location'] = 'London '
    elif(temp.iloc[i][8] == 'Edinburgh Scotland '):
        temp.at[i,'Location'] = 'Edinburgh '
    elif(temp.iloc[i][8] == "European Union ud c uddea ud c uddfa "):
        temp.at[i,'Location'] = 'European Union '
    elif(temp.iloc[i][8] == "France u u e u u u u f "):
        temp.at[i,'Location'] = 'France '
    elif(temp.iloc[i][8] == "London UK  Europe "):
        temp.at[i,'Location'] = 'London '
    elif(temp.iloc[i][8] == "EU "):
        temp.at[i,'Location'] = 'European Union '
    elif(temp.iloc[i][8] == "Germany ud c udde ud c uddea "):
        temp.at[i,'Location'] = 'Germany '
    elif(temp.iloc[i][8] == "Scotland ud c udff udb udc udb udc udb udc udb udc udb udc udb udc f "):
        temp.at[i,'Location'] = 'Scotland '
    elif(temp.iloc[i][8] == "Glasgow Scotland"):
        temp.at[i,'Location'] = 'Glasgow'


#Number of Tweets based on each Location
tweet_location = temp['Location'].value_counts()
tweet_location = tweet_location[tweet_location >= 100]
tweet_location

Not Disclosed             19221
London                     3791
United Kingdom             3074
England                    1806
Scotland                    727
European Union              460
South East England          405
South West England          383
Europe                      372
Manchester                  349
Edinburgh                   310
North West England          266
Wales United Kingdom        223
Glasgow Scotland            203
Ireland                     195
Glasgow                     151
Earth                       138
Liverpool England           138
France                      122
West Midlands England       119
Wales                       118
North East England          118
Bristol England             107
Name: Location, dtype: int64


fig = plt.figure(figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')
plt.plot(tweet_location, marker = "o", linestyle = "--", color = "g")
fig.autofmt_xdate()
plt.xlabel("Location")
plt.ylabel("Total Tweets")
plt.show()
# bar chart more suitable since locations repeated?


#Finding most commonly used words and Hashtags in the tweets
words_in_tweet = [tweet.lower().split() for tweet in temp['Tweet_text']]
all_words = list(itertools.chain(*words_in_tweet))

#Removing most commonly used words in English such as To, I, we, etc so that they don't affect the analysis. We have added a separate dictionary that includes these most common words.
stop_path = 'stop_words_extended.txt'
stop = open(stop_path, "r")
stop_words = []
for word in stop.read().split():
        stop_words.append(word)
stop.close()
all_words = [word for word in all_words if word not in stop_words]


for i in range (0, len(all_words)):
    if (all_words[i] == '#brexit' or all_words[i] == 'brexit.'):
        all_words[i] = 'brexit'
    elif (all_words[i] == 'voted'):
        all_words[i] = 'vote'
    elif (all_words[i] == 'u.k.'):
        all_words[i] = 'uk'


count_words = collections.Counter(all_words)
clean_tweets = pd.DataFrame(count_words.most_common(20),
                             columns=['words', 'count'])
fig, ax = plt.subplots(figsize=(8, 8))
# Plot horizontal bar graph
clean_tweets.sort_values(by='count').plot.barh(x='words',
                      y='count',
                      ax=ax,
                      color="purple")

ax.set_title("20 Most Common Words Found in Tweets")

plt.show()


#Top 15 Tweets with highest number of replies
temp2 = temp.copy()
pd.to_numeric(temp2['Reply_count'])
temp2.sort_values(by='Reply_count', ascending = False, inplace=True)

replies = []
user_name = []
for i in temp2['Reply_count'].head(15):
    replies.append(i)

for i in temp2['Name of User'].head(15):
    name = (remove_punctuation(i))
    if (name == 'Manchmal dumm ud d udd u b ufe f ud e udd c '):
        name = 'Manchmal'
    elif (name == 'Manuela Sch u e big Ostseebarbie '):
        name = 'Manuela'
    elif (name == 'Helen Mole ud c udf GTTO '):
        name = 'Helen Mole'
    
    user_name.append(name)


all_words2 = all_words
#for i in range (0, len(all_words2)):
    #all_words2[i] = remove_usernames(all_words2[i])
    #all_words2[i] = remove_hyperlinks(all_words2[i])
    #all_words2[i] = remove_punctuation(all_words2[i])
    #all_words2[i] = stemming(all_words2[i])


from wordcloud import WordCloud, STOPWORDS
stopwords = set(STOPWORDS)
comment_words = ''
  
comment_words += " ".join(all_words2)+" "
    
stopwords = set(STOPWORDS)
wordcloud = WordCloud(width = 800, height = 800,
                background_color = 'white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)

# plot the WordCloud image                       
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()


fig = plt.figure(figsize = (10, 5))
plt.bar(user_name, replies, color ='maroon', width = 0.6)
plt.xlabel("Name of User")
plt.ylabel("Tweet Reply Counts")
fig.autofmt_xdate()


import re
regexlist=[]
for j in temp['Tweet_text']:
    
    tweet_string = j
#print(tweet_string)
    tweet_string = re.sub(r'@\w+',' ', tweet_string)
    tweet_string = re.sub(r'http\S+','', tweet_string)
    tweet_string = tweet_string.lower()
    tweet_string = re.sub(r'\s+',' ', tweet_string)
    tweet_string = re.sub(r'\b\\u20..',' ', tweet_string)
    tweet_string = re.sub(r'\b\\ud...',' ', tweet_string)
    tweet_string = re.sub(r'\s+',' ', tweet_string)
    tweet_string = re.sub(r"'\'",' ', tweet_string)
    tweet_string = re.sub(r'\\n',' ', tweet_string)
    tweet_string = re.sub(r'[-a-zA-Z0–9@:%._\+~=]{2,256}\.[a-z]{2,6}\b([-a-zA-Z0–9@:%_\+.~?&//=]*)', ' ', tweet_string, flags=re.MULTILINE)
    tweet_string = re.sub(r"\d", "", tweet_string)
    tweet_string = re.sub(r"\Art :", "", tweet_string)
    regexlist.append(tweet_string)
len(regexlist)
temp['Tweet_text']=regexlist


#pip install Textblob


from textblob import TextBlob
#create two new columns specifying polarity and subjectivity of a tweet
emptylist=[]
listp=[]
lists=[]

for k in range(len(regexlist)):
    txtdata = regexlist[k]
    polarity = TextBlob(txtdata).polarity
    subjectivity = TextBlob(txtdata).subjectivity
    listp.append(polarity)
    lists.append(subjectivity)
temp['polarity']=listp
temp['subjectivity']=lists


#. Polarity is essentially measured on a scale from -1 to +1. When polarity of a certain tweet is found to be 0, it is classified as a neutral tweet. Similarly, a polarity of more than 0 indicates towards a 
#positively toned tweet and a polarity below 0 alludes to a negatively sentimental tweet. 
neutral = temp[temp['polarity']==0].shape[0] # neutral tweets
positive = temp[temp['polarity']>0].shape[0] # positive tweets
negative = temp[temp['polarity']<0].shape[0] # negative tweets
print("Number of Neutral Tweets: ", neutral,"\nNumber of Positive sentiment tweets: ",positive,"\nNumber of Negative sentiment tweets: ",negative)

from matplotlib import pyplot as plt

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
plt.title('Sentiment Analysis Pie chart', fontsize=20)
ax.axis('equal')
types = ['Neutral ', 'Positive', 'Negative']
sentiments = [neutral,positive,negative]
ax.pie(sentiments, labels = types,autopct='%1.2f%%')
plt.figure(figsize = (10, 10))
plt.show()

#Are most tweets are subjective or fact based?
sub = temp[temp['subjectivity']>=0.5].shape[0]
per = str((sub/total_tweets1)*100)
print("Percentage of total tweets which are subjective: ", (per)+"%")

Number of Neutral Tweets:  21176 
Number of Positive sentiment tweets:  22388 
Number of Negative sentiment tweets:  18370

<Figure size 720x720 with 0 Axes>

Percentage of total tweets which are subjective:  47.66041269738754%


#Tweets Sentiment in a Radar
import plotly.express as px
temp6 = pd.DataFrame(dict(
    r=[neutral, positive, negative],
    theta=['Neutra', 'Positive', 'Negative']))
fig = px.line_polar(temp6, r='r', theta='theta', line_close=True)
fig.update_traces(fill='toself')
fig.show()
#To further analyze the radar graph, make a circle within the given radar circle


tweet_location1 = temp['Location'].value_counts()
tweet_location1 = tweet_location1[tweet_location1 > 5]
print(tweet_location1)

Not Disclosed                  19221
London                          3791
United Kingdom                  3074
England                         1806
Scotland                         727
                               ...  
That London                        6
UK n n                             6
Horsforth England                  6
Maldon                             6
Dubai United Arab Emirates         6
Name: Location, Length: 1074, dtype: int64


lenn = temp['Location'].shape[0] -1
listeu = ["England", "Denmark", "Belgium","France","Netherlands","Spain","Austria","Poland","Germany","Portugal", "Bulgaria", "Croatia", "Cyprus", "Czech","Estonia", "Finland", "Hungary", "Latvia", "Lithuania", "Luxembourg", "Malta", "Slovenia",  "Sweden"] # add more countries
magnitude =[]

hdf = pd.DataFrame(np.array(listeu),columns=['Countries'])
count=0
for i in range(len(listeu)):
    for j in range(lenn):
        if (temp['Location'][j].find(listeu[i])!=-1):
            count=count+1
    magnitude.append(count)
    count=0


total_tweets_England = temp["Location"].to_list()
counter_eng=0
for z in range(len(total_tweets_England)):
    if total_tweets_England[z].find("England")!=-1:
        counter_eng=counter_eng+1
    elif total_tweets_England[z].find("United Kingdom")!=-1:
        counter_eng=counter_eng+1
    elif total_tweets_England[z].find("United Kingdom")!=-1:
        counter_eng=counter_eng+1
counter_eng

11543


latdf = pd.read_csv("LatLong.txt")
hdf['magnitude']=magnitude
hdf
hdf["yes/no"]=0
latdf["yes/no"]=0
for x in range(len(listeu)):
    for q in range(192):
        if (latdf['Country'][q].find(listeu[x])!=-1):
            latdf["yes/no"][q]=1
            break

#Show countries found
latestdf = latdf[latdf["yes/no"]==1]
latestdf["Magnitude"] = magnitude
latestdf["Magnitude"][59] = counter_eng
latestdf

<ipython-input-47-d1f528704f4d>:9: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-47-d1f528704f4d>:14: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-47-d1f528704f4d>:15: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\ibm t460s\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3418: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


pip install plotly.express

Note: you may need to restart the kernel to use updated packages.

'C:\Users\ibm' is not recognized as an internal or external command,
operable program or batch file.


#GEO MAPPING KIND 1
import plotly.express as px
fig = px.density_mapbox(latestdf, lat='Latitude', lon='Longitude', z='Magnitude', radius=25,center=dict(lat=50, lon=0), zoom=0,mapbox_style="stamen-terrain")
fig.show()
#To zoom and use other controls, toggle mouse pointer to the top right of this cell.


#GEO MAPPING KIND 2
import plotly.graph_objs as go 
from plotly.offline import init_notebook_mode,iplot,plot
init_notebook_mode(connected=True)


data = dict(
        type = 'choropleth',
        colorscale = 'greens',
        locations = latestdf['Country'],
        locationmode = "country names",
        z = latestdf['Magnitude'],
        text = latestdf['Country'],
        colorbar = {'title' : 'Number of Tweets'},
      )
##
layout = dict(title = 'Tracing Tweets to their Locations',
              geo = dict(projection = {'type':'mercator'})
             )


choromap = go.Figure(data = [data],layout = layout)
iplot(choromap,validate=False)
#To zoom and use other controls, toggle mouse pointer to the top right of this cell.
#Write Explanation of plot


#pip install wordcloud


stop = [i for i in all_words if (i == 'stop')]
leave = [i for i in all_words if (i == 'leave')]
remain = [i for i in all_words if (i == 'remain')]
economy = [i for i in all_words if (i == 'immigration')]


print('Total Tweets between 7 and 9:30 on 11th October, 2019:', len(temp))
temp_drop_duplicates = temp.copy()
temp_drop_duplicates.drop_duplicates(subset ="User_id",
                     keep = False, inplace = True)
print('Unique number of Users on twitter who tweeted between 7 and 9:26', len(temp_drop_duplicates))

Total Tweets between 7 and 9:30 on 11th October, 2019: 61934
Unique number of Users on twitter who tweeted between 7 and 9:26 20262


tempp = temp.copy()
tempp.sort_values(by='Reply_count', ascending = False, inplace=True)
tempp = tempp.drop(columns=['Tweet_date', 'Tweet_id_No', 'Tweet_text', 'Tweet_Source',
       'Truncated_Status', 'Name of User', 'Screen Name of user',
       'Location', 'URL', 'Description', 'translator_type', 'Verified',
       'Cumulative_favorites', 'Account_creation_date',
       'geo_enabled_status', 'contributors_enabled', 'is_translator',
       'Extended_tweet', 'Entities', 'Listed_count', 'Friends_count', 
       'Favorites_count', 'polarity', 'subjectivity'])
tempp.head(10)


#Total Number of Tweets of All time from a given user
tempo = temp.copy()
tempo.sort_values(by='Statuses_count', ascending = False, inplace=True)
tempo.drop_duplicates(subset ="User_id",
                     keep = False, inplace = True)

tempo = tempo.drop(columns=['Tweet_date', 'Tweet_id_No', 'Tweet_text', 'Tweet_Source',
       'Truncated_Status', 'User_id', 'Name of User',
       'Location', 'URL', 'Description', 'translator_type',
       'Followers_count', 'Friends_count', 'Listed_count',
       'Cumulative_favorites', 'Account_creation_date', 
       'geo_enabled_status', 'contributors_enabled', 'is_translator',
       'Extended_tweet', 'Entities', 'Reply_count', 'Retweet_count',
       'Favorites_count', 'polarity', 'subjectivity', 'Verified'])
tempo = tempo.head(10)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
x = list(tempo['Screen Name of user'])
y = list(tempo['Statuses_count'])
plt.xlabel("Screen Name of User")
plt.ylabel("Total Tweets")
plt.title('All Time Total User Tweets', fontsize=16)
ax.bar(x, y)
fig.autofmt_xdate()
plt.show()


pip install quilt

Note: you may need to restart the kernel to use updated packages.

'C:\Users\ibm' is not recognized as an internal or external command,
operable program or batch file.


#For identification of Bots/Fake Accounts
newt = temp.copy()
newt = newt.sort_values(by='User_id', ascending = True)
newt = newt.drop(columns = ['Tweet_date', 'Tweet_id_No', 'Tweet_text', 'Tweet_Source',
       'Truncated_Status', 'Screen Name of user',
       'Location', 'URL', 'Description', 'translator_type', 'Verified',
        'Listed_count',
       'Cumulative_favorites', 'Account_creation_date',
       'geo_enabled_status', 'contributors_enabled', 'is_translator',
       'Extended_tweet', 'Entities', 'Reply_count', 
       'Favorites_count', 'polarity', 'subjectivity'])


newt = newt[newt['Followers_count'] < 10]
#newt = newt[newt['Friends_count'] < 1000]
#newt = newt[newt['Retweet_count'] > 1000]
#newt = newt[newt['Statuses_count'] < 8000]
tempti = newt
#tempti.apply(lambda x: pd.to_numeric(x, errors='coerce')).dropna()
tempti


import plotly.graph_objects as go


N = 1000
random_x = tempti.Retweet_count.iloc[:1000]
random_y0 = tempti.Followers_count.iloc[:1000]
random_y1 = tempti.Friends_count.iloc[:1000]
#random_y2 = tempti.Statuses_count.iloc[:1000]

fig = go.Figure()


fig.add_trace(go.Scatter(x=random_x, y=random_y0,
                    mode='markers',
                    name='Followers_count'))

fig.add_trace(go.Scatter(x=random_x, y=random_y1,
                    mode='markers',
                    name='Friends_count'))
'''
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
                    mode='markers',
                    name='Statuses_count'))
'''
title = "Potential Bot Accounts Detection"
fig.show()


from sklearn.linear_model import LinearRegression
x = np.array(tempp.Retweet_count.iloc[:40]).reshape((-1, 1))
y = np.array(tempp.Followers_count.iloc[:40])
model = LinearRegression()
model.fit(x, y)
model = LinearRegression().fit(x, y)
r_sq = model.score(x, y)
print('coefficient of determination:', r_sq)
print('slope:', model.coef_)

coefficient of determination: 0.035617726008222106
slope: [-0.03152843]


#Correlation Matrix Between Different Variables in the Feature Space (Columns in the Dataset):
temp_new = temp.copy()
temp_new = temp_new.drop(columns = ['Tweet_date', 'Tweet_id_No', 
       'Truncated_Status', 'User_id', 'Name of User', 'Screen Name of user',
        'translator_type', 
       'Cumulative_favorites',
       'geo_enabled_status', 'contributors_enabled', 'is_translator',
       'Entities',
       'Favorites_count', 'polarity', 'subjectivity'])

f = plt.figure(figsize=(19, 15))
plt.matshow(temp_new.corr(), fignum=f.number)
plt.xticks(range(temp_new.select_dtypes(['number']).shape[1]), temp_new.select_dtypes(['number']).columns, fontsize=14, rotation=45)
plt.yticks(range(temp_new.select_dtypes(['number']).shape[1]), temp_new.select_dtypes(['number']).columns, fontsize=14)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
plt.title('Correlation Matrix', fontsize=16);


corr = temp_new.corr()
corr.style.background_gradient(cmap='coolwarm')
corr.style.background_gradient(cmap='coolwarm').set_precision(2)


#pip install seaborn - install this library
import seaborn as sns
# new correlation matrix as the above one isnt perceptually understanding
corr_mat = temp_new.corr()
plt.subplots(figsize=(15,8))
sns.heatmap(corr_mat, annot = True, linewidths=3, linecolor='black', cmap = 'Reds')
plt.title('Correlation Plot Between Attributes')
plt.xlabel('Features')
plt.show()


test_list = [i for i in temp_new.iloc[:5000]['Tweet_text']]
res = [(x, i.split()[j + 1]) for i in test_list 
       for j, x in enumerate(i.split()) if j < len(i.split()) - 1]
print ("The formed bigrams are : " + str(res))

The formed bigrams are : [('lady:', 'i'), ('i', 'knew'), ('knew', 'exactly'), ('exactly', 'what'), ('what', 'i'), ('i', 'voted'), ('voted', 'for'), ('for', '...'), ('...', 'lady:'), ('lady:', 'none'), ('none', 'of'), ('of', 'knows'), ('knows', 'what'), ('what', 's'), ('s', 'going'), ('going', 'to'), ('to', 'happ'), ('happ', 'display_text_range:['), ('display_text_range:[', ']'), ('rt', 'ukgovscotland:'), ('ukgovscotland:', 'rt'), ('rt', 'tradegovuk:'), ('tradegovuk:', 'here'), ('here', 's'), ('s', 'your'), ('your', 'step-by-step'), ('step-by-step', 'guide'), ('guide', 'on'), ('on', 'how'), ('how', 'to'), ('to', 'transport'), ('transport', 'goods'), ('goods', 'out'), ('out', 'of'), ('of', 'the'), ('the', 'uk'), ('uk', 'and'), ('and', 'through'), ('remainer', 'fanatics'), ('fanatics', 'tried'), ('tried', 'very'), ('very', 'hard'), ('hard', 'to'), ('to', 'have'), ('have', 'julia'), ('julia', 'hartley-brewer'), ('hartley-brewer', 'dumped'), ('dumped', 'from'), ('from', 'tonight'), ('tonight', 's'), ('s', '#bbcqt.'), ('#bbcqt.', 'the'), ('the', 'arrogance'), ('arrogance', 'and'), ('and', 'intoleranc'), ("i've", 'seen'), ('seen', 'so'), ('so', 'few'), ('few', 'people'), ('people', 'talking'), ('talking', 'about'), ('about', 'this.'), ('this.', 'reason'), ('reason', 'would'), ('would', 'they'), ('they', 'have'), ('have', 'to'), ('to', 'not'), ('not', 'back'), ('back', 'i'), ('rt', 'ukgovscotland:'), ('ukgovscotland:', 'rt'), ('rt', 'beisgovuk:'), ('beisgovuk:', '\\udd'), ('\\udd', '\\ufef'), ('\\ufef', 'not'), ('not', 'sure'), ('sure', 'what'), ('what', 'you'), ('you', 'need'), ('need', 'to'), ('to', 'do'), ('do', 'to'), ('to', 'prepare'), ('prepare', 'your'), ('your', 'business'), ('business', 'for'), ('for', '#brexit'), ('#brexit', 'on'), ('on', 'october?\\u'), ('lied', 'last'), ('last', 'night'), ('night', 'on'), ('on', '#bbcqt'), ('#bbcqt', 'only'), ('only', 'organs'), ('organs', 'were'), ('were', 'received'), ('received', 'from'), ('from', 'the'), ('the', 'eu'), ('eu', 'last'), ('last', 'year.'), ('year.', 'i'), (':', 'vote'), ('vote', 'leave'), ('leave', 'to'), ('to', 'get'), ('get', 'rid'), ('rid', 'of'), ('of', 'red'), ('red', 'tape!\\ud'), ('tape!\\ud', ':'), (':', 'here'), ('here', 's'), ('s', 'a'), ('a', 'mega-thread'), ('mega-thread', 'linking'), ('linking', 'to'), ('to', 'a'), ('a', 'fuck-tonne'), ('fuck-tonne', 'of'), ('of', 'videos'), ('videos', 'about'), ('about', 'all'), ('all', 'the'), ('disinformation', 'is'), ('is', '\\us'), ('\\us', 'speciality'), ('speciality', 'and'), ('and', 'the'), ('the', 'bxp'), ('bxp', 'leader'), ('leader', 'stated'), ('stated', 'putin'), ('putin', 'was'), ('was', 'the'), ('the', 'world'), ('world', 'leader'), ('leader', 'he'), ('he', 'most'), ('most', 'admired'), ('admired', 'in'), ('in', 'the'), ('rt', 'ukgovscotland:'), ('ukgovscotland:', 'rt'), ('rt', 'cabinetofficeuk:'), ('cabinetofficeuk:', 'there'), ('there', 'are'), ('are', 'fewer'), ('fewer', 'than'), ('than', 'days'), ('days', 'to'), ('to', 'get'), ('get', 'ready'), ('ready', 'for'), ('for', 'brexit.'), ('brexit.', 'find'), ('find', 'out'), ('out', 'what'), ('what', 'you'), ('you', 'need'), ('the', '#loancharge'), ('#loancharge', 'is'), ('is', 'the'), ('the', 'big'), ('big', 'political'), ('political', 'scandal'), ('scandal', 'of'), ('of', 'our'), ('our', 'times.'), ('times.', "it's"), ("it's", 'about'), ('about', 'the'), ('the', 'only'), ('only', 'issue'), ('issue', 'that'), ('that', 'unites'), ('unites', 'politicians'), ('politicians', 'across'), ('across', 'the'), ('the', 'd'), ('woman:', '\\i'), ('\\i', 'absolutely'), ('absolutely', 'knew'), ('knew', 'what'), ('what', 'i'), ('i', 'voted'), ('voted', 'for!\\'), ('for!\\', 'theo:'), ('theo:', '\\so'), ('\\so', "what's"), ("what's", 'going'), ('going', 'to'), ('to', 'happen'), ('happen', 'after'), ('after', 'we'), ('we', 'brexit?\\'), ('brexit?\\', 'woman:'), ('woman:', 'obody'), ('obody', 'knows'), ('knows', 'what'), ('what', 'is'), ('woman:', '\\i'), ('\\i', 'absolutely'), ('absolutely', 'knew'), ('knew', 'what'), ('what', 'i'), ('i', 'voted'), ('voted', 'for!\\'), ('for!\\', 'theo:'), ('theo:', '\\so'), ('\\so', "what's"), ("what's", 'going'), ('going', 'to'), ('to', 'happen'), ('happen', 'after'), ('after', 'we'), ('we', 'brexit?\\'), ('brexit?\\', 'woman:'), ('woman:', 'obody'), ('obody', 'knows'), ('knows', 'what'), ('what', 'is'), ('six', 'stages'), ('stages', 'of'), ('of', 'brexit:'), ('brexit:', 'no'), ('no', 'deal'), ('deal', 'is'), ('is', 'an'), ('an', 'impossibility.'), ('impossibility.', 'no'), ('no', 'deal'), ('deal', 'is'), ('is', 'hugely'), ('hugely', 'unlikely.'), ('unlikely.', 'no'), ('no', 'deal'), ('deal', 'has'), ('has', 'to'), ('to', 'be'), ('be', 'a'), ('a', 'possibility'), ('possibility', 'in'), ('in', 'theory'), ('theory', 'b'), ('followed', 'up'), ('up', 'by:'), ('by:', 'thinking'), ('thinking', 'a'), ('a', 'no-deal'), ('no-deal', 'brexit'), ('brexit', '\\gets'), ('\\gets', 'brexit'), ('brexit', 'done\\'), 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('rene', 'display_text_range:['), ('display_text_range:[', ']'), ('varadkar', 'says'), ('says', 'a'), ('a', 'new'), ('new', '\\uctreaty'), ('\\uctreaty', 'agreement'), ('agreement', 'is'), ('is', 'possible'), ('possible', 'by'), ('by', 'the'), ('the', 'end'), ('end', 'of'), ('of', 'october.'), ('october.', 'we'), ('we', 'didn'), ('didn', 't'), ('t', 'vote'), ('vote', 'brexit'), ('brexit', 'to'), ('to', 'leave'), ('leave', 'one'), ('one', 'eu'), ('eu', 'treaty'), ('treaty', 'an'), ('any', 'deal'), ('deal', 'where'), ('where', 'eu'), ('eu', 'common'), ('common', 'defence'), ('defence', 'policy'), ('policy', 'still'), ('still', 'has'), ('has', 'influence'), ('influence', 'over'), ('over', 'our'), ('our', 'armed'), ('armed', 'forces'), ('forces', 'defence'), ('defence', 'equipment'), ('equipment', 'and'), ('and', 'shipbuilding'), ('#french', 'minister:'), ('minister:', 'no-deal'), ('no-deal', '#brexit'), ('#brexit', 'likely'), ('likely', 'if'), ('if', '#britain'), ('#britain', 'does'), ('does', 'not'), ('not', 'seek'), ('seek', 'compromise')]

	Followers_count
Name of User
Reuters Top News	2.077957e+07
The Guardian	8.039751e+06
Bloomberg	5.627978e+06
The Hindu	5.546778e+06
Sky News	5.185423e+06
Financial Times	5.034418e+06
Expansi\u00f3n CNN	3.996868e+06
Noticias24	3.259278e+06
Thairath_News	3.048868e+06
Brian Cox	2.940980e+06

	Country	Code	Latitude	Longitude	yes/no	Magnitude
8	Republic of Austria	AT	47.33333	13.33333	1	7876
14	Kingdom of Belgium	BE	50.75000	4.50000	1	27
16	Republic of Bulgaria	BG	42.66667	25.25000	1	105
41	Republic of Cyprus	CY	35.00000	33.00000	1	610
42	Czechia	CZ	49.75000	15.00000	1	118
43	Federal Republic of Germany	DE	51.50000	10.50000	1	163
45	Kingdom of Denmark	DK	56.00000	10.00000	1	16
50	Republic of Estonia	EE	59.00000	26.00000	1	13
53	Kingdom of Spain	ES	40.00000	-4.00000	1	172
55	Republic of Finland	FI	64.00000	26.00000	1	63
57	Republic of France	FR	46.00000	2.00000	1	9
59	United Kingdom of Great Britain and Northern I...	GB	54.75844	-2.69531	1	11543
71	Republic of Croatia	HR	45.16667	15.50000	1	37
73	Hungary	HU	47.00000	20.00000	1	11
102	Republic of Lithuania	LT	55.41667	24.00000	1	23
103	Grand Duchy of Luxembourg	LU	49.75000	6.16667	1	31
104	Republic of Latvia	LV	57.00000	25.00000	1	15
116	Republic of Malta	MT	35.91667	14.43333	1	4
127	Kingdom of the Netherlands	NL	52.25000	5.75000	1	0
138	Republic of Poland	PL	52.00000	20.00000	1	20
139	Portugal Republic	PT	39.69450	-8.13057	1	18
150	Kingdom of Sweden	SE	62.00000	15.00000	1	1
152	Republic of Slovenia	SI	46.08333	15.00000	1	53

	User_id	Followers_count	Statuses_count	Reply_count	Retweet_count
7257	8.471464e+17	1130.0	35132.0	12712.0	9380.0
7359	2.260620e+09	2.0	28.0	11988.0	27775.0
48299	1.176922e+18	242.0	2645.0	7455.0	20808.0
9305	1.091982e+18	257.0	2778.0	6108.0	6169.0
16312	4.763717e+09	6.0	951.0	4468.0	5058.0
50586	1.345298e+09	762.0	42172.0	4468.0	5058.0
37226	1.491198e+09	347.0	7080.0	4468.0	5058.0
40172	2.832739e+09	11.0	1275.0	4468.0	5058.0
47553	1.088706e+18	19.0	170.0	4417.0	51656.0
4354	2.702818e+09	584.0	33991.0	4417.0	51655.0

	User_id	Name of User	Followers_count	Friends_count	Statuses_count	Retweet_count
9940	1.943962e+07	Nick Houghton	5.0	55.0	247.0	963.0
45539	2.036716e+07	Diane Thurman Leslie	8.0	57.0	1045.0	1324.0
25165	2.248152e+07	douglas travers	9.0	13.0	470.0	0.0
36959	2.274571e+07	jon ward	1.0	20.0	103.0	0.0
29825	2.491124e+07	Vonnie Heffer	7.0	91.0	157.0	8159.0
...	...	...	...	...	...	...
26371	1.182561e+18	PedroSamosa	0.0	5.0	1.0	2355.0
39292	1.182573e+18	Ella Dodd	0.0	12.0	1.0	5700.0
50302	1.182575e+18	Rhea	0.0	0.0	6.0	0.0
54917	1.182582e+18	JustGladItsOver	0.0	17.0	1.0	0.0
58486	1.182583e+18	Definitelynotsargon	0.0	0.0	5.0	0.0

	Followers_count	Friends_count	Listed_count	Statuses_count	Reply_count	Retweet_count
Followers_count	1.00	0.03	0.96	0.11	-0.01	-0.01
Friends_count	0.03	1.00	0.05	0.21	-0.01	-0.03
Listed_count	0.96	0.05	1.00	0.16	-0.02	-0.02
Statuses_count	0.11	0.21	0.16	1.00	-0.02	-0.06
Reply_count	-0.01	-0.01	-0.02	-0.02	1.00	0.35
Retweet_count	-0.01	-0.03	-0.02	-0.06	0.35	1.00

Data Preprocessing¶

Data Cleaning¶

About The DataSet¶

EXPLORATORY DATA ANALYSIS AND VISUALIZATIONS¶

Tweets from verified and unverified accounts¶

Analyzing at which hour tweets were at peak¶

Finding most commonly used words and Hashtags in the tweets¶

Word Cloud of Most Frequently Used Words¶

Highest Tweet Replies and Their Users¶

Sentiment Analysis¶

Tracing Tweets to Their Original Location¶

Total Number of Tweets of All time from a given user¶

POTENTIAL BOT ACCOUNTS¶

REGRESSION ANALYSIS BETWEEN STATUSES COUNT AND RETWEETS DURING THE ISSUE.¶

Bigrams (List of words that were jointly mentioned in the tweets)¶

	Tweet_date	Tweet_id_No	Tweet_id_str	Tweet_text	Tweet_Source	Truncated_Status	Reply_to_Status_ID	Reply_to_status_ID_Str	Reply_to_USER_id	Reply_to_screen_name	...	following	follow_request_sent	notifications	coordinates	place	Extended_tweet	Entities	Reply_count	Retweet_count	Favorites_count
0	Fri Oct 11 07:03:12 2019	1182552212876214272	1182552212876214272	@bbcquestiontime @TheoPaphitis Lady: I knew ex...	\u003ca href=\http:\/\/twitter.com\/download\/...	true	1182420892871249922	1182420892871249922	20699709	bbcquestiontime	...	null	null	NaN	null	null	@bbcquestiontime @TheoPaphitis Lady: I knew ex...	BBCQT	0.0	0	0
1	...	...	...	...	...	...	...	...	...	...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	...	...	...	...	...	...	...	...	...	...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Fri Oct 11 07:03:13 2019	1182552213790609413	1182552213790609413	RT UKGovScotland: RT tradegovuk: Here\u2019s y...	\u003ca href=\https:\/\/ifttt.com\ rel=\nofoll...	true	null	null	null	null	...	null	null	NaN	null	null	RT UKGovScotland: RT tradegovuk: Here\u2019s y...	Brexit	0.0	0	NaN
4	...	...	...	...	...	...	...	...	...	...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
185940	Fri Oct 11 09:26:50 2019	1182588355869057024	1182588355869057024	RT @LeaveEUOfficial: Oasis legend Noel Gallagh...	\u003ca href=\http:\/\/twitter.com\/download\/...	false	null	null	null	null	...	null	null	NaN	null	null	Oasis legend Noel Gallagher serves yet more an...	Oasis legend Noel Gallagher serves yet more an...	168.0	1666	5108
185941	...	...	...	...	...	...	...	...	...	...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
185942	...	...	...	...	...	...	...	...	...	...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
185943	Fri Oct 11 09:26:50 2019	1182588357253259264	1182588357253259264	RT @DavidLammy: Outrageous that @Nigel_Farage'...	\u003ca href=\http:\/\/twitter.com\/download\/...	false	null	null	null	null	...	null	null	NaN	null	null	Outrageous that @Nigel_Farage's @brexitparty_u...	Outrageous that @Nigel_Farage's @brexitparty_u...	250.0	1446	3985
185944	...	...	...	...	...	...	...	...	...	...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN